Sotoudeh Houman, Sadaatpour Zahra, Rezaei Ali, Shafaat Omid, Sotoudeh Ehsan, Tabatabaie Mohsen, Singhal Aparna, Tanwar Manoj
Radiology, University of Alabama at Birmingham School of Medicine, Birmingham, USA.
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA.
Cureus. 2021 Oct 5;13(10):e18497. doi: 10.7759/cureus.18497. eCollection 2021 Oct.
Introduction Ventricular shunting remains the standard of care for patients with idiopathic normal pressure hydrocephalus (iNPH); however, not all patients benefit from the shunting. Prediction of response in advance can result in improved patient selection for ventricular shunting. This study aims to develop a machine learning predictive model for treatment response after shunt placement using the clinical and radiomics features. Methods In this retrospective pilot study, the medical records of iNPH patients who underwent ventricular shunting were evaluated. In each patient, the "idiopathic normal pressure hydrocephalus grading scale" (iNPHGS) and a "Modified Rankin Scale" were calculated before and after surgery. The subsequent treatment response was calculated as the difference between the iNPHGS scores before and after surgery. iNPHGS score reduction of two or more than two were considered as treatment response. The presurgical MRI scans were evaluated by radiologists, the ventricular systems were segmented on the T2-weighted images, and the radiomics features were extracted from the segmented ventricular system. Using Orange data mining open-source platform, different machine learning models were then developed based on the presurgical clinical features and the selected radiomics features to predict treatment response after shunt placement. Results After the implementation of the inclusion criteria, 78 patients were included in this study. One hundred twenty radiomics features were extracted, and the 12 best predictive radiomics features were selected. Using only clinical data (iNPHGS and Modified Rankin Scale), the random forest model achieved the best performance in treatment prediction with an area under the curve (AUC) of 0.71. Adding the Radiomics analysis to the clinical data improved the prediction performance, with the support vector machine (SVM) achieving the highest rank in treatment prediction with an AUC of 0.8. Adding age and sex to the analysis did not improve the prediction. Conclusion Using machine learning models for treatment response prediction in patients with iNPH is feasible with acceptable accuracy. Adding the Radiomics analysis to the clinical features can further improve the predictive performance. SVM is likely the best model for this task.
引言 脑室分流术仍是特发性正常压力脑积水（iNPH）患者的标准治疗方法；然而，并非所有患者都能从分流术中获益。提前预测反应可改善脑室分流术的患者选择。本研究旨在利用临床和影像组学特征开发一种机器学习预测模型，用于预测分流术后的治疗反应。
方法 在这项回顾性前瞻性研究中，对接受脑室分流术的iNPH患者的病历进行了评估。在每位患者中，计算手术前后的“特发性正常压力脑积水分级量表”（iNPHGS）和“改良Rankin量表”。随后的治疗反应计算为手术前后iNPHGS评分的差值。iNPHGS评分降低两个或两个以上被视为治疗反应。放射科医生对术前MRI扫描进行评估，在T2加权图像上对脑室系统进行分割，并从分割后的脑室系统中提取影像组学特征。然后使用Orange数据挖掘开源平台，基于术前临床特征和选定的影像组学特征开发不同的机器学习模型，以预测分流术后的治疗反应。
结果 实施纳入标准后，本研究共纳入78例患者。提取了120个影像组学特征，并选择了12个最佳预测影像组学特征。仅使用临床数据（iNPHGS和改良Rankin量表）时，随机森林模型在治疗预测中表现最佳，曲线下面积（AUC）为0.71。将影像组学分析添加到临床数据中可提高预测性能，支持向量机（SVM）在治疗预测中排名最高，AUC为0.8。将年龄和性别添加到分析中并未改善预测效果。
结论 使用机器学习模型预测iNPH患者的治疗反应是可行的，准确性可接受。将影像组学分析添加到临床特征中可进一步提高预测性能。SVM可能是这项任务的最佳模型。
